Jiaze Cai

This paper describes the development and flight testing of a tail-sitter unmanned aerial vehicle (UAV) platform that has the potential to be tube-launched. Integrated with vertical take-off and landing (VTOL) capability, the platform can perform high-endurance loiter tasks along with low-speed and hovering flights, and land vertically in limited spaces. The current design utilizes a thrust-vectored counter-rotating coaxial propeller combined with a foldable conventional fixed-wing to accomplish both vertical and horizontal flights. A feedback control strategy capable of stabilizing and controlling the vehicle in both vertical and horizontal flight was designed and implemented on a custom-designed 1.7-gram autopilot. Several tests were conducted in hover and edgewise flight with aggressive pilot inputs to demonstrate the controllability of the aircraft even in the presence of moderate wind gusts. Additionally, transition flight tests were also performed, which demonstrated the vehicle’s capability of transitioning from vertical to fixed-wing horizontal flight using thrust-vectoring-based pitch control.

Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is introduced to unlock the versatility and adaptiveness of flapping-wing flight. We propose a model-free reinforcement learning (RL)-based framework for a high degree-of-freedom (DoF) bird-inspired flapping-wing robot that allows for multimodal flight and agile trajectory tracking. Stability analysis was performed on the closed-loop system comprising of the flapping-wing system and the RL policy. Additionally, simulation results demonstrate that the RL-based controller can successfully learn complex wing trajectory patterns, achieve stable flight, switch between flight modes spontaneously, and track different trajectories under various aerodynamic conditions.